基于多源数据的干旱区盐渍地信息提取

土壤盐渍化严重威胁着干旱区绿洲的稳定与可持续发展,因此借助遥感手段快速提取盐渍地信息并及时掌握其空间分布有着重要的现实意义。以塔里木盆地北缘盐渍地普遍发育区域库车绿洲为例,探讨了干旱区以盐生植被红柳为主要覆盖的盐渍地信息的提取方法。综合利用TM卫星图像数据以及Radarsat雷达数据,分析了研究区主要地物的光谱特征及其波段间的相互运算,从而分析盐渍地与其它地物之间的可分性。着重分析了雷达波段作为一个波段加入ETM的6波段中一起参与主成分变换后对盐渍地信息的提取。研究表明：K-L-5（第五主成分）是提取重度盐渍地信息的最佳波段,TM1是区分红柳覆盖区（轻、中度）盐渍地信息的最佳波段,提取盐渍地信息时混分的水体信息可以通过MNDWI（改进归一化差异水体指数）设定一定的阈值予以剔除,混分的植被信息可以通过NDVI设定一定的阈值予以剔除。根据以上分析,建立决策树模型进行盐渍地信息的提取。结果表明,该方法的总体提取效果较好,是干旱区监测盐渍地变化的有效手段。同时也说明由于雷达波段的参与增加了盐渍地与其它地物之间的可分性,为雷达影像在提取盐渍地信息方面提供了一条有效途径。     

汉江上游植被指数变化及其归因分析

植被对调节流域水文过程、维持生态平衡具有重要作用,分析汉江上游植被变化和驱动因子具有重要价值。采用MODIS/Terra的NDVI数据和Land Cover数据分析了汉江上游不同土地利用类型的NDVI的变化规律,结合气象数据和DMSP灯光数据分析了气候变化和人类活动对植被NDVI的影响,分析了植被指数变化的主导性驱动因子。结果表明,2001-2017年汉江上游NDVI整体上呈增加趋势,年均NDVI介于0.5～0.62之间;同种土地利用类型的NDVI年际变化不大,不同土地利用类型的NDVI变化趋势基本一致,NDVI从高到低依次为落叶阔叶林、混交林、草地、农用地、灌丛、常绿针叶林。月平均最高气温是影响月NDVI变化的主导因子,夏季NDVI变化与日照时数和降水量关系较大,冬季NDVI变化与气温相关性较高。气温和降水与NDVI年变化的相关系数高于平均灯光强度变化,即年尺度上气象因素对植被变化的影响高于人类活动。     

基于SEBS模型的老哈河流域蒸散发研究

基于表面能量平衡系统(SEBS)模型,结合NOAA/AVHRR数据,估算半干旱的老哈河流域实际日蒸散发量,并将估算结果与结合FAO-Penman模型和作物系数法计算的参考作物蒸发量进行比较,最后综合分析了老哈河流域蒸散发与土地利用、归一化植被指数(NDVI)以及地表温度(LST)的关系.结果表明:SEBS模型在老哈河流域有较好的适用性;老哈河流域实际日蒸散发时空差异较大,其中7、8月份流域蒸散发量较大,9、10月份蒸散发量逐渐减小,流域西部山区的蒸散发量较大,中部和流域出口所在的平原区相对较小;流域不同土地利用类型蒸散发量不尽相同,其中林地的日平均蒸散发量最高,其次为耕地、灌丛和草地;流域实际蒸散发量与NDVI呈线性正相关,与LST呈线性负相关.     

Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?

In the urban environment both quality of life and surface biophysical processes are closely related to the presence of vegetation. Spectral mixture analysis (SMA) has been frequently used to derive subpixel vegetation information from remotely sensed imagery in urban areas, where the underlying landscapes are assumed to be composed of a few fundamental components, called endmembers. A critical step in SMA is to identify the endmembers and their corresponding spectral signatures. A common practice in SMA assumes a constant spectral signature for each endmember. In fact, the spectral signatures of endmembers may vary from pixel to pixel due to changes in biophysical (e.g. leaves, stems and bark) and biochemical (e.g. chlorophyll content) composition. This study developed a Bayesian Spectral Mixture Analysis (BSMA) model to understand the impact of endmember variability on the derivation of subpixel vegetation fractions in an urban environment. BSMA incorporates endmember spectral variability in the unmixing process based on Bayes Theorem. In traditional SMA, each endmember is represented by a constant signature, while BSMA uses the endmember signature probability distribution in the analysis. BSMA has the advantage of maximally capturing the spectral variability of an image with the least number of endmembers. In this study, the BSMA model is first applied to simulated images, and then to Ikonos and Landsat ETM+ images. BSMA leads to an improved estimate of subpixel vegetation fractions, and provides uncertainty information for the estimates. The study also found that the traditional SMA using the statistical means of the signature distributions as endmember signatures produces subpixel endmember fractions with almost the same and sometimes even better accuracy than those from BSMA except without uncertainty information for the estimates. However, using the modes of signature distributions as endmembers may result in serious bias in subpixel endmember fractions derived from traditional SMA.     

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

A novel ocean color index to detect floating algae in the global oceans

Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.     

基于NDVI的人工植被碳储量估算

人工植被是吸收CO²维护生态系统健康的重要生物成分,干旱区人工碳汇林在CO²减排方面具有重要的作用。应用2009年8月TM数据,提取克拉玛依人工减排林生态景观格局信息,并应用NDVI指数估算植被碳密度。通过测定乔木层及草本层生物量,估算出人工植被乔木层及草本层碳密度。结果表明,克拉玛依人工减排林乔木层的平均碳密度值为37.04 mg/hm²,1 m×1 m样方内草本层平均碳密度为59.65 g/m²,地上植被碳密度约为37.64 mg/hm²,植被层碳储量为250 915.5 mg;随着植被的生长发育及生物量累积效应的发挥,人工植被的碳汇功能还将进一步增大。     

Extraction of Agricultural Phenological Parameters of Sri Lanka Using MODIS,NDVI Time Series Data

Management of crops is an essential part in the food production procedure. Having a thorough knowledge of growth stages of each crop is of paramount importance in this respect. Phenology (transplanting, panicle formation, flowering etc) is the study of cyclic and seasonal natural phenomena that are controlled by environmental and climatic factors. Monitoring the crop condition manually in the field is difficult and time consuming. Therefore recently, several methods have been introduced by using satellite derived vegetation indices. Extraction of phenological parameters is helpful for the purposes like irrigation management, nutrient management, health management, yield prediction and crop type mapping. Easily extracted parameters will be the important data base for agricultural researchers. This research is an attempt to extract paddy phenological parameters of Sri Lanka by using 16 years’ (2000 to 2015) Time series MODIS Normalised Difference Vegetation Index (NDVI), which is highly sensitive for the green vegetation and the data were analysed using SPIRITS and TIMESAT software's. Periodicity converter in SPIRITS and Savitzky Golay filtering in TIMESAT and SPIRITS are helpful in smoothing the time series which are perturbed by noise due to missing values and Clouds. Phenology is considered as a sensitive climate change indicator but, it is very essential to have a comprehensive familiarity about the method of water supply that the study area is irrigated or rain fed so as to eliminate the wrong interpretation. As results, average of long time series of NDVI profile for a few agro ecological zones of Sri Lanka with extracted seven parameters (Start of the season, End of the season, Length of the season, Booting date, Base value, Maximum NDVI during the Season, Amplitude) and generated phenological parameter maps are presented here. The crop phenology is a very important element of agricultural monitoring, to ensure the security of the food crop production.     

基于MODIS NDVI的中国东部植被覆盖时空变化特征

基于2001~2010年逐月MODIS NDVI产品,采用Mann-Kendall检验和Hurst指数,研究中国东部及5个子区(东北区、黄淮海区、长江中下游区、江南区和华南区)植被覆盖的时空动态特征。结果表明:10 a期间中国东部植被覆盖以不显著改善和不显著退化特征为主,前者稍占优势,4个季节中秋季改善状况最显著;未来态势主要表现为不显著改善且未来将持续改善和不显著退化且未来将持续退化两种特征。植被覆盖为不显著退化且将持续退化的区域主要分布在大型城市或城市群周围;5个子区中,除江南区年内主要表现为不显著退化且将持续退化特征外,其余4区均主要表现为不显著改善且未来将持续改善特征。     

典型地表对长沙主城区地表温度的影响分析

城市热岛是一种城市地区温度比郊区温度高的现象,它可改变城市的自然和社会过程,引发一系列环境问题.利用Landsat 8 TIRS10波段的单通道算法(TIRS10_SC算法)反演了长沙主城区2013年7月、2016年3月、7月和11月4景Landsat 8影像的地表温度,并进一步分析了地表温度的时空分布特征,建设用地、...